AI0-001 Practice Test 21 — 15 Questions

Question 1

A team is building a document intelligence application that extracts key fields from invoices. They have 10,000 labeled invoices. What is the first step in the AI project lifecycle?

Accepted Answer

Problem definition – specify which fields to extract and accuracy targets. The first step in any AI project lifecycle is problem definition, which involves clearly specifying the business problem, the desired outcomes, and the success criteria. In this case, the team needs to define which fields to extract from invoices and set accuracy targets before proceeding to data preparation, model selection, or acquisition. Without a clear problem definition, subsequent steps lack direction and measurable goals.

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Model selection – choose a pre-trained vision transformer

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Data preparation – clean and normalize the invoice images

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Data acquisition – collect additional invoices from public sources

Question 2

A hospital uses an AI system to predict patient deterioration from vital signs. The system currently uses a logistic regression model trained on data from the past year. Recently, the hospital adopted a new patient monitoring device that provides more accurate readings. The model's performance has dropped significantly. The data science team has access to the new device's data for the past month and wants to improve the model with minimal disruption. The team also wants to ensure the model remains interpretable for regulatory compliance. Which approach should they take?

Accepted Answer

Retrain the logistic regression model on a combined dataset of old and new device data. Retraining the logistic regression model on a combined dataset of old and new device data is the best approach because it leverages all available data to adapt the model to the new device's measurement distribution while preserving the model's inherent interpretability. Logistic regression is a linear model that remains fully transparent for regulatory compliance, and combining both datasets helps the model learn the systematic shift in vital sign readings without discarding valuable historical patterns. This minimizes disruption by avoiding a complete overhaul and directly addresses the performance drop caused by the change in input data distribution.

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Continue using the current model and manually adjust predictions based on device differences

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Build an ensemble of logistic regression and a neural network using new data only

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Replace the logistic regression model with a gradient boosting model using only new device data

Question 3

A team is deploying an anomaly detection system for real-time monitoring of server metrics. The system should alert when metrics deviate significantly from normal patterns. Which type of AI model is MOST suitable?

Accepted Answer

Autoencoder neural network. An autoencoder neural network is an unsupervised learning model that learns to compress and reconstruct input data. When trained on normal server metrics, it will accurately reconstruct normal patterns but produce high reconstruction error for anomalous patterns, making it ideal for anomaly detection. This allows the system to alert when metrics deviate significantly from normal.

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Recommendation system model

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Linear regression model

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Image classification model

Question 4

Which TWO of the following are common activation functions used in deep neural networks?

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a common activation function in deep neural networks because it introduces non-linearity while being computationally efficient, outputting the input directly if positive and zero otherwise. It helps mitigate the vanishing gradient problem, making it a default choice for hidden layers in many architectures.

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Linear Regression

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Support Vector Machine

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K-means

Question 5

A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?

Accepted Answer

Apply regularization. The model shows high training accuracy (95%) but significantly lower test accuracy (60%), which is a classic sign of overfitting. Regularization (Option C) directly addresses overfitting by adding a penalty term to the loss function (e.g., L1 or L2 regularization), discouraging the model from learning overly complex patterns that do not generalize. This is the first step because it targets the core issue without requiring additional data or increasing complexity.

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Collect more data

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Use cross-validation

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Increase model complexity

Question 6

A financial institution needs to deploy a credit scoring model that is interpretable to regulators. The model must provide clear reasons for each decision. Which model type should the institution choose?

Accepted Answer

A glass-box model such as logistic regression or a decision tree. A glass-box model such as logistic regression or a decision tree is inherently interpretable, meaning its internal decision logic can be directly examined and explained to regulators without post-hoc approximation. For credit scoring, regulators often require clear, auditable reasons for each decision (e.g., adverse action notices under ECOA/Regulation B), and glass-box models provide this natively. This makes them the correct choice when interpretability is a hard requirement.

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A gradient-boosted tree ensemble with SHAP explanations

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A black-box model with a model card describing its behavior

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A deep neural network with LIME explanations

Question 7

An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?

Accepted Answer

Deploying explainability tools. Deploying explainability tools (B) is the most directly relevant governance practice because the regulation specifically requires explanations for automated decisions. Explainability tools, such as LIME or SHAP, generate human-interpretable justifications for model outputs, enabling compliance with transparency mandates. This directly addresses the need to understand and communicate why a particular decision was made, unlike other practices that focus on privacy, fairness, or oversight.

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Implementing differential privacy

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Conducting bias audits

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Establishing an AI ethics board

Question 8

A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by perturbing inputs and learning a linear surrogate. SHAP provides Shapley values, which are also local but game-theoretic. Attention visualisation is for transformer models. Model cards describe global model behaviour.

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Model card

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SHAP

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Attention visualisation

Question 9

A data science team is building a binary classifier to detect fraudulent transactions. The dataset has only 2% fraud cases. Which data preparation technique is MOST critical to address this imbalance?

Accepted Answer

Apply synthetic minority oversampling (SMOTE) to the training set. With only 2% fraud cases, the dataset is severely imbalanced, which can cause the classifier to be biased toward the majority class (non-fraud) and achieve high accuracy without learning to detect fraud. SMOTE (Synthetic Minority Oversampling Technique) addresses this by generating synthetic examples of the minority class (fraud) in the training set, balancing the class distribution and improving the model's ability to generalize to fraud cases. This is the most critical technique among the options because it directly tackles the class imbalance problem, which is the primary challenge in this scenario.

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Use one-hot encoding on categorical features

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Remove outliers from the transaction amounts

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Normalize all numerical features to have zero mean and unit variance

Question 10

A financial services firm deploys an AI system to screen loan applications. The model was trained on historical data that reflected biased lending practices. After deployment, a regulatory body investigates and finds that the model denies loans at a disproportionately higher rate to a protected demographic group. The firm must address this issue while maintaining compliance with fair lending laws. The Chief AI Officer proposes four possible actions. Which action is the most appropriate first step?

Accepted Answer

Retrain the model using a debiased dataset and implement fairness-aware algorithms, then validate with fairness metrics. The most appropriate first step is to remediate the model itself by retraining on a debiased dataset, applying fairness-aware algorithms, and validating with fairness metrics. This addresses the root cause — biased historical training data — rather than only documenting or disclosing the problem. It also aligns with fair lending laws that require the model's outcomes to be non-discriminatory, not merely reported.

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Document the model's predictions and submit a report to the regulator explaining the historical dataset bias

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Immediately deploy a rule-based system to manually review all denial decisions from the AI system

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Disclose the bias findings to all rejected applicants and offer them priority reconsideration

Question 11

A medical diagnosis AI uses a model trained on sensitive patient data. The team wants to allow researchers to query the model but must protect against membership inference attacks. Which mitigation is MOST effective?

Accepted Answer

Use differential privacy during model training. Differential privacy during model training (Option D) is the most effective mitigation because it formally bounds the influence any single patient record can have on the model's parameters. By adding calibrated noise to the training process (e.g., via DP-SGD), the model's outputs become provably insensitive to the presence or absence of any individual data point, directly thwarting membership inference attacks that try to determine if a specific patient's data was used in training.

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Encrypt the model weights

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Add noise to model outputs at inference time

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Limit the number of queries per researcher

Question 12

Which of the following is a key advantage of using ONNX (Open Neural Network Exchange) format for model deployment?

Accepted Answer

It enables framework interoperability for model inference. ONNX provides a standardized, open format for representing machine learning models, enabling seamless interoperability between different frameworks (e.g., PyTorch, TensorFlow, scikit-learn). This allows a model trained in one framework to be deployed for inference using a different runtime or hardware accelerator without requiring retraining or manual conversion, which is a key advantage in heterogeneous production environments.

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It automatically quantizes models to INT8

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It compresses model size by 90%

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It reduces training time

Question 13

A company uses a third-party pre-trained language model for a sentiment analysis API. They want to ensure the model has not been backdoored. Which supply chain security practice is MOST effective?

Accepted Answer

Obtain and verify a Software Bill of Materials (SBOM) for the model. An SBOM provides a formal, verifiable inventory of the model's components, dependencies, versions, and provenance, which is the foundation for detecting tampering or unauthorized modifications in the AI supply chain. Verifying the SBOM against trusted hashes or signatures lets the company confirm the model artifact they received matches what the vendor published, catching backdoors injected during development or distribution. This is the recognized supply chain security control for third-party AI artifacts.

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Monitor API usage for anomalous patterns

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Use federated learning to train the model

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Implement differential privacy during training

Question 14

Based on the exhibit, what is the most likely issue with the trained model?

Accepted Answer

Overfitting because training accuracy is much higher than validation accuracy. The exhibit shows a significant gap between high training accuracy and lower validation accuracy, which is the classic symptom of overfitting. The model has memorized the training data rather than learning generalizable patterns, leading to poor performance on unseen validation data.

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Data leakage artificially inflating training accuracy

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Vanishing gradients causing no learning

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Underfitting due to insufficient epochs

Question 15

A company deploys a computer vision model for quality inspection on a manufacturing line. After deployment, the model's accuracy drops from 95% to 80% over two weeks. Which action is most likely to address this issue?

Accepted Answer

Retrain the model using recently collected production data.. The accuracy drop over two weeks indicates data drift or concept drift, where the production data distribution changes over time. Retraining the model with recently collected production data realigns it with the current data distribution, directly addressing the drift. Option B (increasing confidence threshold) may reduce false positives but does not fix the underlying drift and could lower recall. Option C (decreasing learning rate) is irrelevant for inference; it only affects training and cannot be applied post-deployment to fix drift. Option D (deploying an ensemble) adds computational overhead and does not resolve drift; it might even mask the issue without correcting it.

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Increase the confidence threshold for predictions.

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Decrease the learning rate of the training algorithm.

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Deploy an additional ensemble of models for redundancy.